We present experiments for different search spaces for ImageNet classification: DONNA, EfficientNet-Compression and MobileNetV3 (1.0\(\times \) , 1.2\(\times \) ). The latter two search spaces are blockwise versions of the spaces considered by OFA [1]}; that is, parameters such as expansion ratio and kernel size are modified on the block level rather than the layer level, rendering the overall search space coarser than that of OFA. Selected results for these spaces are discussed in this section, more extensive results can be found in Appendix REF . We first show that networks found by DONNA in the DONNA search space outperform the state-of-the-art (Figure REF ). For example, DONNA is up to \(2.4\%\)  more accurate on ImageNet [2]} validation compared to OFA[3]} trained from scratch with the same amount of parameters. At the same time, DONNA finds models outperforming DNA [4]} up to 1.5\(\%\)  on a V100 GPU at the same latency and MobileNetV2 (\(1.4\times \) ) by 10\(\%\)  at 0.5\(\%\)  higher accuracy on the Samsung S20 GPU. We also show that MobileNetV3-style networks found by DONNA achieve the same quality of models compared to Mnasnet [5]} and OFA [3]} when optimizing for the same metric (See Fig. REF  and Tab. REF ). All experiments are for ImageNet [2]} images with \(224\times 224\)  input resolution. Training hyperparameters are discussed in Appendix REF .
